





'''simple docstring'''






import os

from bleurt import score  # From: git+https://github.com/google-research/bleurt.git

import datasets


lowerCAmelCase									=   datasets.logging.get_logger(__name__)


lowerCAmelCase									=   """\
@inproceedings{bleurt,
  title={BLEURT: Learning Robust Metrics for Text Generation},
  author={Thibault Sellam and Dipanjan Das and Ankur P. Parikh},
  booktitle={ACL},
  year={2020},
  url={https://arxiv.org/abs/2004.04696}
}
"""

lowerCAmelCase									=   """\
BLEURT a learnt evaluation metric for Natural Language Generation. It is built using multiple phases of transfer learning starting from a pretrained BERT model (Devlin et al. 2018)
and then employing another pre-training phrase using synthetic data. Finally it is trained on WMT human annotations. You may run BLEURT out-of-the-box or fine-tune
it for your specific application (the latter is expected to perform better).

See the project's README at https://github.com/google-research/bleurt#readme for more information.
"""

lowerCAmelCase									=   """
BLEURT score.

Args:
    `predictions` (list of str): prediction/candidate sentences
    `references` (list of str): reference sentences
    `checkpoint` BLEURT checkpoint. Will default to BLEURT-tiny if None.

Returns:
    'scores': List of scores.
Examples:

    >>> predictions = [\"hello there\", \"general kenobi\"]
    >>> references = [\"hello there\", \"general kenobi\"]
    >>> bleurt = datasets.load_metric(\"bleurt\")
    >>> results = bleurt.compute(predictions=predictions, references=references)
    >>> print([round(v, 2) for v in results[\"scores\"]])
    [1.03, 1.04]
"""

lowerCAmelCase									=   {
    """bleurt-tiny-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-128.zip""",
    """bleurt-tiny-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-tiny-512.zip""",
    """bleurt-base-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-128.zip""",
    """bleurt-base-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-base-512.zip""",
    """bleurt-large-128""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-128.zip""",
    """bleurt-large-512""": """https://storage.googleapis.com/bleurt-oss/bleurt-large-512.zip""",
    """BLEURT-20-D3""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D3.zip""",
    """BLEURT-20-D6""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D6.zip""",
    """BLEURT-20-D12""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20-D12.zip""",
    """BLEURT-20""": """https://storage.googleapis.com/bleurt-oss-21/BLEURT-20.zip""",
}
@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION   , _KWARGS_DESCRIPTION							)
class 		lowerCamelCase	(   datasets.Metric							):



	def 	_lowerCamelCase     ( self						):
			return datasets.MetricInfo(
			    description=_DESCRIPTION		,   citation=_CITATION		,   homepage="https://github.com/google-research/bleurt"		,   inputs_description=_KWARGS_DESCRIPTION		,   features=datasets.Features(
			        {
			            "predictions": datasets.Value("string"		,   id="sequence"						),
			            "references": datasets.Value("string"		,   id="sequence"						),
			        }						)		,   codebase_urls=["https://github.com/google-research/bleurt"]		,   reference_urls=["https://github.com/google-research/bleurt", "https://arxiv.org/abs/2004.04696"]		,   )



	def 	_lowerCamelCase     ( self		,   a_						):
			# check that config name specifies a valid BLEURT model
			if self.config_name == "default":
					logger.warning(
					    "Using default BLEURT-Base checkpoint for sequence maximum length 128. "
					    "You can use a bigger model for better results with e.g.: datasets.load_metric('bleurt', 'bleurt-large-512')."						)
					lowerCAmelCase  :     str       						= "bleurt-base-128"

			if self.config_name.lower() in CHECKPOINT_URLS:
					lowerCAmelCase  :     Any       						= self.config_name.lower()

			elif self.config_name.upper() in CHECKPOINT_URLS:
					lowerCAmelCase  :     str       						= self.config_name.upper()

			else:
					raise KeyError(
					    F'''{self.config_name} model not found. You should supply the name of a model checkpoint for bleurt in {CHECKPOINT_URLS.keys()}'''						)

			# download the model checkpoint specified by self.config_name and set up the scorer
			lowerCAmelCase  :     str       						= dl_manager.download_and_extract(CHECKPOINT_URLS[checkpoint_name]						)
			lowerCAmelCase  :     Union[str, Any]       						= score.BleurtScorer(os.path.join(a_		,   a_						)						)





	def 	_lowerCamelCase     ( self		,   a_		,   a_						):
			lowerCAmelCase  :     Union[str, Any]       						= self.scorer.score(references=a_		,   candidates=a_						)
			return {"scores": scores}










'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


lowerCAmelCase									=   {
    """configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowerCAmelCase									=   [
					    """GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
					    """GraphormerForGraphClassification""",
					    """GraphormerModel""",
					    """GraphormerPreTrainedModel""",
					]


if TYPE_CHECKING:
					from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_graphormer import (
										    GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
										    GraphormerForGraphClassification,
										    GraphormerModel,
										    GraphormerPreTrainedModel,
										)


else:
					import sys

					lowerCAmelCase									=   _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



